Since its launch, the Top 100 has demonstrated the influence and reach that it is possible for research to have – and through our data we have shown that communicating beyond the academy is critical to the broader dissemination and application of academic study. 

Given the evolving landscape of research communication and analyses, we have decided it’s time to refresh our annual review of society’s engagement with research. Instead of continuing to produce a typical ‘Top 100’, in future years we will focus our analyses on the unique opportunities our data offers: identifying emerging trends, highlighting examples of great engagement, and encouraging researchers to communicate their research and its outcomes as effectively as possible. 

As a result of this change we did not produce an official Top 100 for 2021. 

As a final farewell to the original lists, Altmetric’s Head of Data Insights, Mike Taylor, shared a series of blog posts that each took a different approach to compiling a ‘top 100’ – and analyzed the data to draw out the key considerations of each approach. 

• Old School Remix
• Feat. Subjects
• Techno Remix

We recently held a webinar with Mike to talk through the three different approaches in a bit more detail. 

During the session, Mike looked at the similarities and differences between the different sets of data that the calculations returned and considered the pros and cons of each approach. 

We even asked our audience which approach they preferred! 

Watch some key clips from the session here. 

The post The Altmetric Top 100 re-imagined: a discussion with Mike Taylor appeared first on Altmetric.

1. Article-level metrics: indicators that aim to measure the reach or influence of an individual research output such as a journal article or data set. Example: number of citations
2. Journal-level metrics: indicators that aim to measure the reach or influence of a journal. Example: Journal Impact Factor
3. Author-level metrics: indicators that aim to measure the reach or influence of an individual author. Example: h-index

Journal-level and author-level metrics are quite straightforward, but article-level metrics, also known as output-level metrics (because any scholarly output with a unique identifier be tracked), tend to cause a bit of confusion.

The confusion often stems from a misunderstanding of the term altmetrics. Altmetrics stands for alternative metrics, not article-level metrics. The two are not synonymous. Altmetrics are indicators of non-traditional attention and engagement with scholarly outputs, such as attention on social media or in the mass media. 

The Altmetric Attention Score is one example of an article-level metric. Other examples include: citation counts, Relative Citation Ratio (RCR), Category Normalized Citation Impact (CNCI), and Field Weighted Citation Impact (FWCI). 

Then there are usage metrics. Usage metrics include page views, downloads, and shares. Some people categorize usage metrics as a type of article level metric, while others count usage metrics as an entirely different category. At Altmetric, we do not track usage metrics.

Here is an image that may help you visualize how article-level metrics and altmetrics are related:

It’s important to remember to use these metrics responsibly and for their intended use. For example, you shouldn’t use a journal-level metric to assess the performance of an individual. 

To learn more about the responsible use of metrics, consider reading The Leiden Manifesto for Research Metrics¹. Visit the Metrics Toolkit to learn more about different types of metrics and their limitations, data sources, appropriate use cases, and more.  

1. Hicks, D., Wouters, P., Waltman, L. et al. Bibliometrics: The Leiden Manifesto for research metrics. Nature 520, 429–431 (2015). https://doi.org/10.1038/520429a

The post Metrics terminology appeared first on Altmetric.

We have now officially tracked over 200 million mentions of nearly 20 million research outputs. 

These 200 million mentions have come from a huge variety of sources, including social media, blogs, Wikipedia, policy documents, patents, news outlets and more!

We are constantly working to expand our coverage, add sources and discover new mentions. During 2021 we made some fantastic additions…

Expanding Wikipedia Coverage

At the beginning of 2021, we had implemented tracking for the English, Finnish and Swedish versions of Wikipedia. However, we are now tracking mentions across 12 different versions of Wikipedia. 

During 2021 we added tracking for mentions on Chinese, Dutch, French, German, Italian, Japanese, Portuguese, Spanish and Turkish Wikipedia. If a research output is mentioned across pages and posts on any of these Wikipedia versions, these mentions will be picked up and appear in Details Pages, Badges and Attention Scores. To date, we have collected over 6.5 million mentions from Wikipedia.

We are looking forward to adding even more languages to our Wikipedia coverage in the coming months, so stay tuned for our announcements. 

Expanding Policy Source Coverage 

Having visibility over whether a research output has been mentioned in a policy source document provides valuable insights into the societal impact of a piece of research. 

During the past year, Altmetric has added tracking for lots of different policy sources. We made some great and varied additions, from multinational organizations like the Organisation for Economic Co-operation and Development (OECD) and the European Universities Association, to rich local sources such as the Swedish Agency for Health Technology Assessment and Assessment of Social Services (Sweden), Department of Enterprise, Trade and Employment (Ireland), Pharmaceutical Management Agency (New Zealand), and many more! 

Overall, we’ve now tracked a monumental 2,838,208 mentions of research outputs in policy documents – giving researchers, institutions, publishers and R&D companies alike an unparalleled insight into how their research is influencing and informing real-world change. 

UK Clinical Trials 

Altmetric tracks attention to trials from clinicaltrials.gov using National Clinical Trial IDs or NCT IDs. 

In an exciting new development, we are currently working on tracking attention by DOI for trials in the ISRCTN registry, a primary clinical trial registry recognised by WHO and ICMJE. 

This will further expand the insights that can be gleaned from investigating the conversations surrounding clinical trials, even before they are published! 

We recently held a webinar on how you can search and explore clinical trials within the Altmetric Explorer, and the recording is available to watch now. 

Looking ahead

We are thrilled to have added so many diverse mention sources over the past year and each one has contributed to us now reaching this incredible milestone of 200 million mentions.

As we’ve mentioned above, we have got lots of exciting developments and additions planned for the year ahead and we are looking forward to sharing them with you all. 

Make sure you’re following us on Twitter, LinkedIn and Facebook or signed up to our mailing list so you never miss one of our announcements. 

All the best for the year ahead! 

The Altmetric Team

The post 200 million mentions: Altmetric has now officially tracked over 200 million mentions of nearly 20 million research outputs appeared first on Altmetric.

In place of our usual Top 100, our Head of Data Insights, Mike Taylor, is paying tribute to the Top 100 of years past with a series of blog posts that each take a different approach to compiling a ‘top 100’.  This time Mike is taking a new approach that uses techniques that are usually used to iron out differences between citation data: subject normalization.

The Techno Remix

For those of you who have been following this series so far, you’ll recall that the original Top 100 (The Old School Remix) was based solely on the Altmetric Attention Score. Generally speaking, this was successful…until COVID arrived, and dominated scientific communications, like no other subject has done before (although Zika research showed many of the same signs, it didn’t take off in quite the same way). 

So last year, we put aside the old Score-based approach and decided to divide the Top 100 into a series of discipline-based Top 5s – and this functioned well, enabling us to celebrate research from around the academy. 

This year has been very similar: the Old School Top 100 for 2021 would have contained 98 COVID articles, and last year’s approach just 29. 

In my third (and final!) Top 100 I’m putting aside both approaches in favour of a new method that’s inspired by the work of scientometricians from the last decade.

Broadly speaking, citation performance is different by discipline, format and age (just like altmetrics!) and researchers have sought to normalize these variations by developing field-normalized metrics. Examples include the Field-Weighted Citation Index (FWCI), the Relative Citation Ratio (RCR) and the Field Citation Ratio (FCR).

Although these all have differences in their approach, they all attempt to compare a publication’s performance with similar other publications. Typically this means calculating an average citation rate for other documents from the same year, same subject area and in the same format (eg. chapter or article).

For the first time (that I’m aware of), I’ve calculated a similar range of metrics across all Altmetric research outputs going back to 2010, using the Fields of Research taxonomy. This has given us the ability to present a Top 100 that’s based on this normalized Score Index, that doesn’t divide up the results by discipline, but does take discipline into account as part of the calculation.

I’ve also taken the liberty of presenting two Top 10s (for monographs and preprints to illustrate how these metrics might work in practice). 

The big question for today is: does this work? Does this approach produce a Top 100 that fairly represents research from 2021, but appropriately celebrates well-shared publications using a well-known scientific approach?

My final blog post is scheduled to appear in the New Year – here I’ll be dissecting the three approaches, illustrating some commonalities and differences and arguing the merits of each approach. 

Methodology and data

As I mentioned above, my approach to calculating this new metric, that endeavours to reduce the field-based differences between publications, is informed by the work of scientometricians on many other metrics.

The first step is to calculate average values over groups of publications. For example, I take the set of preprints published in 2015, in the field of Cardiorespiratory Medicine (1102): there’s 124 of these indexed by Dimensions. Of these, 100 have Altmetric Attention. Using our data, I can calculate a lot of metrics: the most useful ones are the percentage of publications with attention, and the average. I’ve picked out a few values below:

Table 1: Expected Altmetric values for Preprints, published in 2015, in the field of Cardiorespiratory Medicine

To illustrate how the numbers vary, here are some more values – this time from Oncology (but also preprints, from 2015):

Table 2: Expected Altmetric values for Preprints, published in 2015, in the field of Oncology

For me, it’s interesting to note that while the percentage of preprints (proportion) with attention is similar for all four metrics, the mean values are very different.

So why do age and discipline matter so much? Preprints are very well accepted in some fields (Astronomy, Economics, etc), but much less in other fields (at least, until recently, COVID has seen a marked growth in medical preprints – although where this is sustained or applicable to other topics within medicine is unknown). And some fields age at different rates: humanities are much slower to get attention than AI; the latter will have likely had all the impact it’s likely to have after two years, whereas the humanities are just getting started. The same is true when comparing books and preprints: books are much slower than articles or preprints – so all these data points are important when calculating these values.

The next stage(*) is relatively straightforward: for each article, you look up the article by age, subject area and format, and divide what it actually got by what it might be expected to get. So a Preprint in Oncology from 2015 with a score of 4 gets an index of 4 / 8.49, or 0.47. We can say, then, that it is not performing as well as would be expected. 

We can use the same approach to calculate values for journals, and funders, and universities, and of course, we can use it to compare OA and non-OA research too.

*The next stage isn’t quite the next stage. Many publications sit inside more than one subject area, and to calculate their benchmark values, we need to use one of a number of approaches. The FWCI, for example, calculates the mean value for each subject area/year/format, and then combines them using an Harmonic Mean. The FCR takes a similar approach, using a Geometric mean. Currently, my values are calculated on the basis of the fractions. So a journal that is 80% sports science and 20% food nutrition is compared against 0.8 of the sports science averages and 0.2 of the food nutrition. But this isn’t the time or place to dive into that detail!

The science bit

At last, the Top 100 Techno Remix! Is anyone surprised that COVID also dominates this chart? Probably not: of the 100 research outputs rising to the top using the normalized approach, 63 reference COVID (this includes a couple that aren’t about COVID, but are being referenced within that context). Interestingly, this places this Top 100 directly in the middle of the Old School variety (98 COVID publications) and last year’s approach (29 COVID publications). 

You can download the list here, or view a public report here, and for Altmetric subscribers, here’s the link to the results in Explorer. 

I’ve presented a breakdown of the subject areas associated with the publications (I’ll do this for all of the Top 100s in my final blog post)

(Don’t forget that each publication can have up to 4 subject codes.)

Others*: Getting one publication each were the following subjects:  Linguistics, Cultural Studies, Sociology, Studies in Human Society, Business and Management,  Commerce, Management, Tourism and Services, Paediatrics and Reproductive Medicine, Cardiorespiratory Medicine and Haematology, Nanotechnology, Communications Technologies, Maritime Engineering, Manufacturing Engineering, Food Sciences, Environmental Engineering, Chemical Engineering, Computation Theory and Mathematics, Agricultural and Veterinary Sciences, Geophysics, Geology, Earth Sciences, Chemical Sciences.

Away from COVID, some of the usual suspects appeared: the paper on “cauliflower breath”, the archaeological paper on the Tunguska-sized meteorite, the rotating hip joint, plate tectonics and linguistic analysis of ExxonMobile’s climate change rhetoric are on both this version of the Top 100 and the last one. Climate change remains important, with 5 publications, as do Robotics and AI/emotions. The humanities and arts are less well-served by this approach – several of the interesting publications that were highlighted on the “Top 100 Feat. Subjects” with a focus on  gender and race don’t appear in this version. Animals, however, remain important, with both parrots and ducklings making this list.

To some extent, this Top 100 better represents actual research output. This table, from Dimensions, shows the breakdown of publications by Field of Research code (just the top level, two digit codes):

The table below compares the percentage breakdown of all 2021 publications (4395975!) by FoR with the percentage breakdown of this Top 100 by FoR.

This formulation of the Top 100 got a lot more attention than the Top 100 Feat. Subjects, including 1.5M Tweets, 35k News stories and Blog posts, and close to 200 Policy citations. Top institutions included: University of Oxford, Massachusetts Institute of Technology, Stanford University, Imperial College London, University of Cambridge, Stanford University and Huazhong University of Science and Technology. Institutions from other countries were represented, including Brazil, Mexico, Korea. I’ll run a further analysis on this in my final blog post.

The top 12 journals (and 1 preprint repository!) were:

• New England Journal of Medicine,  10 articles with 211,256 mentions
• MMWR: Morbidity & Mortality Weekly Report, 8 articles with 161,934 mentions
• The Lancet, 7 articles with 108,754 mentions
• Nature, 5 articles with 28,562 mentions
• Science, 5 articles with 65,890 mentions
• Nature Communications, 3  articles with 56,018 mentions
• Scientific Reports, 3  articles with 48,250 mentions
• JAMA Network Open, 2 articles with 44,641 mentions
• JAMA, 2 articles with 43,562 mentions
• Lancet Infectious Diseases, 2 articles with 40,501 mentions
• Nature Human Behaviour, 2 articles with 20,875 mentions
• Science Advances, 2 articles with 1048 mentions
• arXiv, 2 articles with 12,378 mentions

Top 10 Monographs

Having spent so much time focussing on (and celebrating) the most discussed research from 2021, I thought it’d be worthwhile also preparing a final Top 10 – to mark the most discussed Monographs. These represent a very different view into research, with the University of Michigan Press clearly hitting the nail on the head with their “Coronavirus Politics” monograph. I’m particularly cheered to see so many books from the Humanities and Social Sciences appear!

Conclusion

From my perspective, I’m really happy with the way that this final Top 100 has performed. Using a well-understood technique that borrows from existing scientometric literature, we’ve produced a Top 100 that required very little curation (just weeding out retracted articles, editorials and letters), but one that fairly represents research output. Yes, it’s disappointing to see some of the articles from the second version drop out, but I feel that having a split between 2/3rds COVID and 1/3rd non-COVID is a reasonable outcome, given how much attention the pandemic has inevitably attracted.

In the new year, I’ll be pulling apart the three approaches in a little more depth, and offering a more rigorous analytical view. If you’d like to join us on Twitter, you can find us at @altmetric and @herrison. Alternatively, you can comment below, or drop me an email at m.taylor@digital-science.com

Our purpose in producing three Top 100s was to provoke discussion – and we look forward to hearing from you!

Happy holidays, from Mike at Altmetric Data Insights.

The post Top 100 2021 – Techno Remix appeared first on Altmetric.

As we shared in our November 10th blog, we have decided not to produce our traditional Top 100 this year. We’re taking the year off to rethink and refresh our annual review of society’s engagement with research to produce a format that focuses our analyses on the unique opportunities our data offers: identifying emerging trends, highlighting examples of great engagement, and encouraging researchers to communicate their research and its outcomes as effectively as possible.

In place of our usual Top 100, our Head of Data Insights, Mike Taylor, is paying tribute to the Top 100 of years past with a series of blog posts that each take a different approach to compiling a ‘top 100’.  This time Mike is taking the approach we used with the 2020 Top 100 and seeing how the data from this year plays out.

Our approach to the Top 100 in 2020 was informed by the pandemic, and the effect that discussions and the impact of COVID research had on the volume of attention paid to the pandemic, as well as the degree to which science was discussed on social media and in media outlets. The effects of the pandemic on research communication and engagement are still being seen: in our last Top 100 (the Old School Remix), where we ranked research purely by the Altmetric Score, we found that 98 of the top 100 pieces of research were related to COVID work. 

In response to the overwhelming level of COVID-related research last year, our Top 100 approach was to use our subject area classification (based on the Fields of Research taxonomy) to break down the top 100 into 20 groups of 5 high performing articles. We – broadly speaking – followed the same process as earlier algorithms, by excluding letters and editorials.

The FoR taxonomy actually has 22 top level classifications, so you may be wondering why we had 20 categories. When we looked at the data last year, we decided that there was a reasonable amount of overlap between Engineering and Technology, and Life Sciences and Environmental Sciences so we combined those areas. We’ve repeated that approach this year.

This algorithm yielded a much more diverse Top 100 than the original algorithm, and while that was generally well-received, there are definitely some methodological challenges in this approach that I’ll elaborate on below.

Methodology and data

The methodology for this approach (which I’ve informally called the ‘Feat. Subject Areas’) uses a combination of the Altmetric Attention Score, plus the Fields of Research classification, plus a similar set of criteria as for earlier lists (for example, not including letters or editorials). You can find the link to the data on Figshare here, there’s a public summary here and – for Explorer subscribers – the full list is available here, as a saved search.

One of the immediate challenges of this process comes from our tagging process: some articles don’t have any subject area that can be assigned to them within an acceptable level of confidence (it’s driven by a machine classification process), whilst others can have up to four subject areas. In most of our product use-cases, this assignment isn’t an issue: in fact, it’s an advantage that correctly reflects the mix of areas that relate to a research topic, but in the case of this approach to the Top 100, it definitely adds in an area of complexity.

The subject area “Education” threw this process into clarity for me: three of the five articles originally in the Top 5 weren’t really focussed on ‘Education’. Rather they were epidemiological studies of COVID transmission in schools, with virtually no examination of educational policy or practise, or effect on teachers or pupils. Given that we can only accept five articles per subject area, and the purpose of this approach is to create a Top 100 that isn’t dominated by COVID and instead reflects the work within the subject areas, it seems reasonable to rank these three articles as Biological or Medical Sciences, rather than Education.

This curative process was repeated elsewhere: the article that I’ve come to know fondly as “cauliflower breath” could have been in the Top 100 by any one of three subject areas – I felt that Agricultural and Veterinary Sciences was the best classification, but we can discuss that in the comments section. Just not too closely, if you’ve been eating cauliflower.

The science bit

Perhaps unsurprisingly, COVID dominates this Top 100 too, with 29 mentions in the Top 100 Feat. Subjects – but what is interesting is the diversity of the COVID focussed research, and how the focus of research in the last year represents the development of COVID science. If 2020 was the year of understanding the virus, preventing it with a vaccine and experimenting with therapies, 2021 was the year of understanding our reaction to the pandemic – how did governance react, did lockdowns work, what stresses were brought to light in science, what causes skepticism. Interestingly, this shift in focus was not so obvious in the traditional, solely score-based ranking, which maintained its strong focus on vaccines and experimental therapies. Elsewhere in the Top 100, three papers study remote working (but without mentioning COVID – in the titles, at least!), probably influenced by the pandemic.

Just looking at the COVID papers, we get an interesting breakdown. Of particular interest are studies looking at the effectiveness of policy, lockdowns and school closures.

Looking beyond COVID at the other subjects that had more than one article represented (remembering that some papers cover two areas), we can see some really interesting insights into the research that demands our attention. Climate Change is of long-standing importance, research featuring animals, human ancestors and space likewise. This year, a number of papers looked at various areas of inequality, including gender, race, immigration and poverty. Four of the papers featured engaging multimedia – the tectonic plate animation from our last Top 100 makes a reappearance, as does a recording of a prehistoric musical instrument. Food – including our stinky brassica article – features in three articles.

So what attention did the Top 100 Feat. Subject Areas receive?

• Over 660,000 tweets and retweets linked to one of these papers
• Almost 17k news story and blogs posts cited the research
• 187 videos were posted on YouTube
• 223 Wikipedia citations were made
• 36 Policy citations have been detected so far

The top 12 journals were:

• Proceedings of the National Academy of Sciences of the United States of America, 7 articles, with 30,348 mentions mentions,
• Science, 7 articles, with 14,625 mentions,
• Current Biology, with 4 articles, 11,220 mentions,
• Nature, 4 articles, with 10,313 mentions,
• Nature Communications, with 4 articles 57,252 mentions,
• Scientific Reports, 4 articles with 43,549 mentions,
• Nature Astronomy, 2 articles with 871 mentions,
• Nature Human Behaviour, 2 articles with 20,819 mentions,
• PLOS ONE, 2 articles with 378 mentions,
• Science Advances, 2 articles with 5,697 mentions,
• The Journal of Law, Medicine and Ethics, 2 articles with 3,408 mentions,
• Transportation Research, Part D, 2 articles with 1,995 mentions.

The academic institutes in this version of the Top 100 feature a rich mix of organizations. The University of California has 13 articles, MIT and the University of Oxford have six each. Others that caught my eye were Pfizer, American Natural History Museum, Diamond Light and the Royal Horticulture Society. The list is very international, with India, China, Japan, Sweden, Spain, Austria, and Chile all included in the list.

One of the implications of this approach is that there’s a much broader distribution of scores within the Top 100: I’ve plotted the average scores per subject area below.

This graph does provide us with some valuable contextual information to understand the ramifications of choosing to acknowledge subject area variations – and perhaps gives us an idea of the stakeholder communities that are engaged with research from different areas. 

Preprints were not found in this Top 100 analysis either. Better luck next time!

Conclusion

Building on subject area variations noted above, my next Top 100 will introduce a new methodology which uses normalisation approaches more frequently found in citation-based metrics. In this case, I’ll compare articles’ actual Altmetric Attention Score with an expected Attention Score, based on their mix of subject codes. The resultant index (or ratio) is similar in form to the Field Citation Ratio, Relative Citation Ratio of Field-Weighted Citation Index, in that a value of 1.00 suggests an article is performing “as expected”, a value of 100.00 suggests performance one hundred times better than expected, or 0.5 is half as well as expected. It’s hoped that this approach will yield a Top 100 that reflects a more diverse range of research than the original Top 100 approach, but with a more comprehensible underpinning of data.

The post Top 100 2021 – Feat. Subjects appeared first on Altmetric.

Many of you will be familiar with the annual Altmetric Top 100. Launched in 2013, this list highlighted the most-mentioned scholarly publications from the year, and featured much-discussed topical research that had caught the wider public’s imagination. 

Since its launch, the Top 100 has demonstrated the influence and reach that it is possible for research to have – and through our data we have shown that communicating beyond the academy is critical to the broader dissemination and application of academic study. 

Given the evolving landscape of research communication and analyses, we have decided it’s time to refresh our annual review of society’s engagement with research. Instead of continuing to produce a typical ‘Top 100’, in future years we will focus our analyses on the unique opportunities our data offers: identifying emerging trends, highlighting examples of great engagement, and encouraging researchers to communicate their research and its outcomes as effectively as possible. 

As a result of this change we will not be producing an official Top 100 this year.  

But fear not! As a final farewell to the original lists, Altmetric’s Head of Data Insights, Mike Taylor, will be sharing a series of blog posts that each take a different approach to compiling a ‘top 100’ – and analysing the data to draw out the key considerations of each approach.  

Following the blog series we will be in touch with various customers and users within the community to obtain their thoughts, ideas and inputs to help us shape the future report.    

Read on for Mike’s first take on the data this year… 

Top 100 2021 – the Old School Remix

Last year, we decided to approach the Top 100 from a new perspective, by introducing classification by subject areas: this was, in part, a response to the circumstances thrown at us by COVID19, and also a need to be informed by the insights that have emerged following a decade of academic research into altmetrics.

The extent to which altmetrics vary by subject discipline is extremely well known, and was one of the first phenomena to have been thoroughly investigated by researchers in the early days of altmetrics. The fact that differences existed at all wasn’t a surprise: citation counts are also known to vary by discipline. In fact, there are more types of variation: not only does the volume of citation count vary, but so does the speed and duration of citations change. The same is also true for altmetric data: with the added complexity that we have to account for over a dozen sources of attention – all of which have their own unique variations. 

In the years pre-COVID, the Top 100 was based entirely on the Altmetric Attention Score, while last year we took the choice to segment our list based on discipline.

This year, uniquely, we’ve chosen to present three alternative views into the Top 100. In this – the first – blog, I’m revisiting the methodology we followed in the years preceding 2020, and looking solely at the Altmetric Attention Score.

Methodology and data

The methodology for this approach (which I’ve informally called the ‘Old School Remix’) only allows primary research articles – whether published in preprint archives or journals. In other words, we’re not looking at books, editorials, commentaries, reviews, and letters. We also chose to discount articles that have been retracted. You can find the link to the data on Figshare, there’s a public summary and – for Explorer subscribers – the full list is available, as a saved search.

The science bit

The Top 100 Old School Remix sheds light on one of the motivations for introducing subject areas last year: of this year’s top 100 research articles, 98 of them concern COVID19. This shouldn’t be too much of a surprise: pandemics happen rarely, thank goodness, but – by their nature – they can affect everyone in the world.

The two articles that aren’t about COVID19 deserve a special mention.

Bedriñana-Romano et al’s paper, Defining priority areas for blue whale conservation and investigating overlap with vessel traffic in Chilean Patagonia, using a fast-fitting movement model published Open Access in Scientific Reports elegantly combines conservation, real-time / big data, aquaculture and marine telemetry in a very accessible piece of research.

Merdith et al’s paper, Extending full-plate tectonic models into deep time: Linking the Neoproterozoic and the Phanerozoic, published in Earth-Science Reviews is possibly less accessible: both in terms of its subject matter, and not being Open Access. Nevertheless, the team of researchers brilliantly created a video that summarised their paper, condensing 1B years of plate tectonics into 40s of animation.

To have published articles in the second year of this pandemic that have stimulated so much public engagement is no mean feat.

So what attention did the Old School Remix Top 100 get?

• Over 2M tweets and retweets linked to one of these papers
• Almost 50k news story and blogs posts cited the research
• 482 videos were posted on YouTube
• 283 Wikipedia citations were made
• 277 Policy citations have been detected so far

The top 10 journals were:

• New England Journal of Medicine (17 articles, receiving 326k mentions)
• Morbidity and Mortality Weekly Report (13 articles, receiving 217k mentions)
• The Lancet (13 articles, receiving 275k mentions)
• Science (6 articles, receiving 137k mentions)
• JAMA (5 articles, receiving 81k mentions)
• Lancet Infectious Diseases (4 articles, receiving 55k mentions)
• Nature (4 articles, receiving 65k mentions)
• European Journal of Clinical Investigation (2 articles, receiving 68k mentions)
• International Journal of Infectious Diseases (2 articles, receiving 33k mentions)
• JAMA Network Open (2 articles, receiving 45k mentions)

The academic institutes in this version of the Top 100 were strongly international, with South African and Brazilian universities all featuring, representing the global effort the pandemic required. Highlights include the University of Oxford (13 outputs); London School of Hygiene and Tropical Medicine (7); Harvard University (6), University of the Witwatersrand (4), Centers for Disease Control and Prevention (4). University of California, San Diego (4), Pfizer (3), Hospital de Clínicas de Porto Alegre (3), and Sechenov University (3).

Within the 98 COVID articles, some trends are emerging. Whereas last year, the focus was largely on the origins, proteins and genetics of the SARS-CoV-2 virus, on the treatment of the COVID19 disease and the emerging vaccines, this year has largely focussed on the continuing vaccine development and public health policies. To help breakdown these articles, I created a simple taxonomy:

Vaccine research that received attention was much less focussed on novel vaccines, and much more on the long-term effectiveness, the extent to which they protect individuals, the degree to which they have reduced the prevalence of the virus and their effectiveness against variants-of-concern, such as Delta. Public Health research reported methods of transmission, examined the effectiveness of stay-at-home policies and other approaches. Research into the possible use of drugs such as ivermectin and hydroxycholoroquine received a lot of attention around the world. Two areas to receive much more attention in 2021 than in 2020 are masks and Long COVID. The benefits – to the population and the self – of wearing a mask were very hard to quantify early on: however, the science is now solid. Masks work. Sadly, for those who do catch the virus, Long Covid is also a reality, and researchers looked at physical, psychological and neurological issues following the initial treatment period.

Preprints, for all the importance they have played in COVID research, were not to be found in the Top 100.

Conclusion

The Old School Remix Top 100 does not lie: for the second year running, COVID research continued to be the area that has commanded the highest rates of interest. The simple fact is that COVID is of importance to all humans, and (as we might say) has ‘gone viral’ in all the attention sources we cover. Add to that, the speed at which research has been translated into policy has changed – possibly forever – the way in which we might see science expressed in society. But this might also give the impression that it’s the only area to gather attention, and that’s not a fair representation of what happened to scholarly communications in 2021: in fact, while COVID dominated biomedical-focussed attention sources, other areas of research have been actively discussed and continue to be influential.

My next Top 100 will revisit the methodology of last year’s Top 100, where we used subject area classifications to highlight the top most discussed research from a broader range of research.

The post Top 100 2021 – the Old School Remix appeared first on Altmetric.

It’s been a big year for science news, and the Altmetric Top 100 shows us the 100 biggest science stories of the year – the articles that got the most attention online. Released every year, the Altmetric Top 100 highlights the research published during the year that has generated significant international online attention and discussion.

This year we’ve widened the net, and the research includes a whole range of published items – not just academic articles, but also patents, public policy documents and research published in the mainstream media, on blogs, Wikipedia and social media platforms.

The Altmetric Top 100 has been released every December for seven years, and this year has seen the most widely shared story to date: the most discussed academic paper of 2019 was about ‘deepfake’ AI from Samsung that brings the Mona Lisa to life – and can create a video of you from just one still photo.

Read the most discussed study of 2019: Few-Shot Adversarial Learning of Realistic Neural Talking Head Models

This study leads the list, which includes several other tech stories – including an AI image generator that can invent photos, Google claiming quantum supremacy and a device that can translate brain signals into speech.

Themes of 2019

Technology is just one of many themes running through the list, and some of them are familiar from previous years. Catherine Williams, COO of Altmetric, said: “It’s fascinating to see the trends that shape the Top 100 list each year. In 2019, it’s clear that our current climate emergency and political polarization are a matter of huge public concern and debate. This list demonstrates the critical role that research plays in those conversations.”

Understandably, climate change has dominated the science headlines. In the fourth most discussed publication, more than 11,000 scientists from around the world declared “clearly and unequivocally that planet Earth is facing a climate emergency.” At number nine came a potential solution: planting 1.6 billion hectares of trees could soak up more than 200 gigatons of carbon.

We have also been keen to understand more about politics and psychology, which is hardly surprising given some of the political events seen around the world in recent months and years. One study revealed the people most likely to believe the ‘birther’ conspiracy theory that Barack Obama was not born in the US, and another shed light on the proliferation of fake news during the 2016 US election. We also learned what Brexit might mean for healthcare in the UK.

Animals caught our attention too – including humans. The Top 100 includes research about insects, birds, puppy dog eyes, how to reanimate a dead brain, a pregnancy with a transplanted womb, cats, zebra-painted cows and, controversially, the ‘gay gene’.

One overarching theme relates to our very survival: How do we mitigate the challenges associated with modern life and be healthy?

Surviving modern life

This theme shows up throughout the Top 100, and it’s been prominent every year. In this year’s list, there is research on the impact of junk food diets, on the effects of our distance from nature, the impact of us consuming sweet drinks, the implications of using e-cigarettes and the problem of air pollution cutting our lifespans short.

Soda popped up a few times in the Top 100 this year. Research covering ten European countries showed that people who drink soda have a higher risk of dying early. And choosing the diet option isn’t better, it’s just different – while consuming sugary soda is linked to death from digestive disease, artificially sweetened soft drinks were linked to death from circulatory disease.

In an interview for The Altmetric Podcast, the lead author of one study in this theme shared her story. Looking at data from 80,000 postmenopausal women, a team led by Dr. Yasmin Mossavar-Rahmani at Albert Einstein College of Medicine in the US found that consuming artificially sweetened drinks was linked to an increased risk of stroke, heart disease and early death.

Read the study: “Artificially Sweetened Beverages and Stroke, Coronary Heart Disease, and All-Cause Mortality in the Women’s Health Initiative”

Dr. Mossavar-Rahmani was running a randomized clinical trial to find out whether a healthy diet can reduce the risk of cognitive decline in people with Alzheimer’s disease, and she as interested in whether drinking artificially sweetened drinks had an effect on cognition. Data from the Women’s Health Initiative provided plenty of insights, and some associations between consuming diet drinks and higher risk of certain health problems.

Compared with women who consumed diet drinks less than once a week or not at all, women who had two or more servings a day were more at risk of stroke, heart attack and death: 23% were more likely to have both fatal and non-fatal stroke, 31% were more likely to have ischemic stroke and 29% were more likely to develop heart disease, both fatal and non-fatal.

What’s more, 16% were more likely to die from any cause. “It also seemed to have some association with how long you live,” said Dr. Mossavar-Rahmani. “That kind of intrigued us, so we decided to delve deeper into the data, and we looked at women without any previous heart disease or diabetes. Again, we saw that they were 2.44 times more likely to have ischemic stroke.”

The associations were numerous and clear: higher consumption of diet drinks is associated with higher risk of stroke and heart disease. But as clear as the association is, it’s not causation.

“I want to emphasize even though we see these associations, it doesn’t imply causation – it doesn’t mean that diet drinks necessarily increase the risk with small artery occlusion which is associated with cognitive decline,” said Dr. Mossavar-Rahmani. “And while the risk of stroke is higher, the actual absolute risk is small: the incidence rate is like getting two strokes per thousand people per year. The question is, where does that leave us? And so what we think is we just need more research.”

Small steps to healthy living

This can be seen throughout the theme. There are indications that we might be healthier if we stop eating junk food, eat whole grains, go vegetarian, walk more, exercise, eat fewer eggs and drink our tea a bit cooler. But very few studies provide clear, conclusive guidelines for what we should eat and how we should behave in order to thrive in the world today.

That in itself is a great reflection of science: each study should be taken in the context of the body of research it sits on, rather than being held up on its own. That’s the strength of empiricism: evidence is built over time, becoming stronger the more we know.

However they’re presented, studies like this that seemed to have an important message for our health hit the headlines. According to Dr. Mossavar-Rahmani, it was the fact that so many of us consume diet drinks that made her story compelling. “It’s something that everybody’s having these days, and it touches everyone’s life. I just didn’t realize there would be so much interest, I was so amazed… it was a really incredible exposure for someone who’s frankly publicity shy.”

One team that might not want to revisit their study a second time was a group of doctors and healthcare professionals who each swallowed a Lego head… to save us from the dirty job of searching through kids’ poop.

“We all work in hospitals and know that it’s a really common problem that children come in having swallowed stuff,” explained author Dr. Tessa Davis from the Royal London Hospital in the UK. “We thought we’d use the opportunity to do a bit of education, and also inject a bit of humor into the world.”

Read the study: “Everything is awesome: Don’t forget the Lego”

Six people each swallowed a Lego head and tracked their progression. The average time it took to pass through (measured using the Found And Retrieved Time, or FART score… cue giggles) was 1.71 days, except for one participant, who is apparently still hosting the head somewhere.

But there’s a serious message in there too. While most things will pass through kids’ systems harmlessly, some objects are dangerous, particularly button batteries. “That’s something that needs to be removed quickly and that you definitely need to go to hospital for, so it gave us the opportunity to put some education out there as well,” said Dr. Davis.

It worked: the paper went viral, landing the research on prime time TV, courtesy of James Corden and Jimmy Fallon. “It was absolutely incredible. It really will be a career highlight; ironically, I suspect this may be the thing that we’ll be remembered for.”

A year of top tips for promoting research

Every month, we’ve asked the authors we have interviewed for their top tips for promoting research. Here they are, in a nutshell:

• Mossavar-Rahmani: Submit your manuscript to an organization that has the ability to publicize your research.
• de la Iglesia: Combine a clear message with data that supports that message.
• Manson: Think about which audience you’re trying to reach; having a very responsible press release is helpful to increase the likelihood that the story will be covered in a balanced way.
• Brent Loken: To make a global impact, work on your communications, whether it’s interview skills, media skills or writing press releases.
• Hviid: Publish in the big journals, and do something to not be forgotten immediately after you publish.
• Prof McShane: Focus on looking for the truth and publishing replicable findings… searching around for hot topics or to make a splash may actually be antithetical to the idea of finding the truth.
• Kaminski: Be open to really engage, but do the things that you’re comfortable with.
• Bastin: Use the little bit of energy you still have after submitting a paper into trying to write a nice summary and to reach the media.
• Skowron: Have a good press release package, with nice materials that reporters can use to write an article.
• Phaedra Cress: If you’re not on social media, jump in.
• Protzko: Take advantage of public relations offices that are available at your university, and be clear about the message.

Until next year, happy reading!

The post Modern life, climate change and technology: discover the biggest science stories of 2019 appeared first on Altmetric.

1. Finding the altmetrics for your content

There are several ways you can find altmetrics for your publications, and not necessarily just the metrics provided by Altmetric.com. Many publishers now include the Altmetric badges on their article pages, and others sometimes include information pulled from elsewhere, such as their own media or social monitoring service (check out PLOS and Mendeley for examples of this). 

Where you do see the Altmetric badges embedded on a page you can click on them to access the full record of attention – the Altmetric details page – so you can see not just how much your work was talked about, but who was doing the talking and what they were saying.

Using the free Altmetric bookmarklet will also give you access to these pages for items that have a DOI, or if your institution subscribes to the Altmetric Explorer you can explore all of the mentions for over 9 million outputs in one place!

Whichever route you choose, the data will be updating in real-time, providing you with the latest insights on the conversations and engagement surrounding your work.

2. What is the data telling you?

Once you’ve found your altmetrics, the next thing to do is to critically evaluate the data. Which channels is your work receiving attention from? Has it been shared by an influential Tweeter, resulting in a broader reach? Is it being used by students or in Syllabi? Have policy makers picked it up and referenced it in their advice, or has someone thought it relevant enough to include in a Wikipedia article?

Determining who is talking about your research, and which channels they’re using to do so, can help you understand why they are likely giving it that attention, even if they don’t explicitly state it.

Another thing to consider is when your work received the attention it’s got – was it as soon as it was published, or did something happen later that meant it was surfaced again (for example a world event or topical news story?) Has funding changed in that area at the same time? Did someone do something that meant that it was suddenly noticed? You might be surprised by some of the attention that older work often gets, even if at the time it did not seem so notable.

One question that researchers often ask is whether or not their Altmetric Attention Score (the weighted count of attention an item has received) is any good. Although the score does not measure quality; of the research, the researcher, or the attention; it can be useful for measuring reach and visibility. A higher score = higher reach.

To help you see how the score your item has received compares to other work published at the same time (or in the same journal) the Altmetric details pages include a ‘score in context’ section. Taking note of the information in this tab can help you see if there might be opportunities for your work to be made more visible, or if in fact it’s way outperforming other content in your field.

If you’re publishing Open Access it can be interesting to keep an eye on how that impacts the attention your work receives – preliminary research has found that OA items sometimes get shared more than non-OA – another great reason to make your research open and discoverable!

3. How can you determine what to do next?

So, great, you’ve got a good understanding of how people are talking about your research, why they’re likely doing so, and how that compares in terms of volume of engagement to other work in your field. BUT, what are people actually saying about that other work? If it’s had less attention, who is it from? Are other researchers being picked up less by a general audience but more by policy makers? If it’s had more attention, is it attention from audiences that you would like to reach?

Using the tools outlined in section 1 you can start to explore the altmetrics for other work in your discipline, as well as your own. Browsing some recent issues of journals or the latest book releases will enable you to see how the level of visibility differs between publishers and title.

Based on what you uncover you can start to make a plan. Try answering these questions as a start:

• Do you feel the amount of visibility and engagement your work got was appropriate to the content?
• Who were you aiming would see and benefit from your work? From the altmetrics you have, does that seem to have been achieved?
• Compared with other research in your field, does the engagement your work has received seem high, average, or low? Are there areas where you might improve outreach?
• What ‘impacts’ or other outcomes did you hope for as a result of doing and publishing your work? Does the data provide any evidence of the extent to which you’ve reached those goals? (Perhaps, for example, you wanted to raise awareness of your topic in a certain country, and through the altmetrics for the publication you can see people from that region Tweeting about the work)
• Did you publish it open access, or would you perhaps want to consider doing so in future?

Altmetrics, like any metrics, are only valuable if you put them into context. Make sure you’ve got a good understanding of the qualitative data, and align what you find with what you want to achieve.

4. Take action!

Congratulations! You’ve published your work, taken a look at your early altmetrics, and given some consideration to whether or not you’ve got the result that you want. Now what? In some cases you could take immediate action: if you feel a past publication could’ve been more visible, why not share it now? You could identify some key bloggers to reach out to (based on the altmetrics for other publications in your field) or even just take the opportunity to re-share the work from your own social accounts.

If you’ve had something accepted but are still waiting on a publication date then now is the ideal time to try something new! Connect with your research support librarian, scholarly comms office or the PR team at your publisher to discuss how you might maximise the engagement for your work, or even just give some thought to what you’d like to achieve and build your own small plan. It doesn’t need to be extensive, and tools like Kudos can help make it even easier.

If you’re yet to submit then what you’ve learnt can help you shape future strategies: which publishers will share your work most effectively? Are they present on the channels and amongst the audiences you want to reach? What support do they offer for helping you share the work, and tracking the engagement?

Another thing to consider might be what research you make available. Altmetrics aren’t just for articles – they also pick up the attention for datasets, posters, images and all sorts of other research hosted on publisher websites, institutional repositories, preprint servers and other platforms. Sharing more and gathering evidence to demonstrate what the results of that were can help tell the bigger story of who you are as a researcher.

5. Make it easy

We all have loads of demands on our time, and keeping an eye on every little mention won’t always be top of your to-do list. Aim to make integrating altmetrics into your workflows as easy as possible – the Altmetric details pages have a link to sign up for alerts to be notified when new mentions of your publications occur, and if you’re using the Altmetric Explorer you can set up daily, weekly or monthly updates for any search.

If you check in on your citations from time to time, why not also take a moment to take a look over your altmetrics?

We hope you find this post useful! If you’d like some top tips for simple ways to make all of your work more visible click here to download our handy guide – and share your own in the comments below or on Twitter @altmetric!

The post Interpreting altmetrics appeared first on Altmetric.

Here, I’ve shared some expert recommendations for the best quantitative and qualitative data to collect in your digital library assessment efforts, as well as tools you can use to streamline data collection. Altmetrics services are obviously an important part of your assessment toolkit, and this post describes both altmetrics services and DIY approaches you can use to collect this genre of data.

For a deeper look at creating a full-on assessment strategy, check out this white paper I co-authored last year with the ever-awesome Michelle Dalmau and David Scherer (upon which this post is based), or join me, Grace Costantino (Biodiversity Heritage Library), and Molly Bragg (Duke University) at the ACRL 2017 workshop for a chance to get hands-on in creating your own strategy.

What’s there to measure in digital libraries?

For digital special collections, librarians often track metrics for both digitized objects and descriptive information related to those objects, including:

• Compressed and uncompressed versions of images, videos, and audio files;
• Text files (usually in XML, PDF, and HTML formats);
• Descriptive information about the object, captured in a variety of metadata standards; and
• Contextual information about an object or a collection described in accompanying essays, timelines, and visualizations.

Put simply, it would come as no surprise to anyone who has worked with digital collections to say that digital library content is heterogeneous and, in many cases, complicated to measure.

Measuring more and better: suggested data to collect

The list below is not comprehensive, but instead reflects metrics that are commonly used and easily tracked, and which can be used to understand attention paid to digital library content by various audiences.

Quantitative metrics

Quantitative metrics are arguably easier to capture than other types of data. They do not measure exactly how digital library items are used, but instead they are indicative of overall volume of interest in digital library content.

• Page views are commonly tracked in web analytics software, and are often called “impressions”. Along with downloads, they can convey that items are being successfully exposed across the broader Web. Accurately tracking downloads can be problematic for digital special collections that are stored in digital object repositories and are referenced via persistent URLs (PURLs).
• Visits, especially returning visits, can provide some indication of engagement.
• Referring sites straddle the quantitative and qualitative realms in that a subset of referring sites serve as some indication of scholarly (i.e., Wikipedia and Google Scholar) or popular relevance (i.e., news outlets and Reddit) that could be further traced for citations and references in context.
• Shares on social media signal the circulation of content across a potentially vast network of people.
• Saves, Favorites, and Bookmarks can capture interest in a given item, and in some cases the intent to use items in future teaching.
• Adaptations relate to the creation of derivative works: new research or creative works based on existing digital library images, data, research, software, etc.  Accurately tracking adaptation is difficult to do, even in systems that provide mechanisms for doing so (i.e. forking in GitHub). For digital library content that requires end users to manually offer attribution (i.e. citing a digital collection in a book chapter), the problem can be more pernicious.
• Requests for hi-resolution digital library content, submitted via automated means, could be an indicator of later citations or reuse and adaptations. Further study is needed.
• Citations help us understand the use of our digital libraries in a scholarly context, particularly when cited in books and journal articles. Citations to digital library content can be difficult to uncover, however.
• Visitor demographic information is another metric of interest to libraries. Demographic information like age and user interests can be sourced from third-party services like Facebook or Google (which are sometimes used to allow visitors to login to library websites), from IP addresses that help determine users’ location, or even from library-administered surveys. There are obvious privacy implications to tracking visitors’ demographic information.

Qualitative data

The use of qualitative data for assessment sometimes require manual collection and review, or personal engagement with a digital library’s users. It is invaluable in its ability to convey intent, especially when used alongside quantitative data.

• Mentions can be as informal as a “shout-out” or as formal as a citation, though in either case the mention may not be constructed in easily traceable ways (i.e., citing a canonical URL or persistent identifier).  In venues like Twitter and Wikipedia, where mentions are more easily tracked and aggregated, this data can be easily harvested to better understand context: what is being said about a particular item? And who is involved in the discussion? Mentions can be appear on the Web in many forms:  course syllabi, blog posts, policy documents, and news articles (just to name a few).
• Reviews or comments provide another avenue for determining value.  The volume of comments often does not matter as much as the nature of the comments.  In addition, a commenter’s identity can sometimes be equally important when analyzing comment content.
• Reference inquiries often provide a story of scholarly use and engagement beyond web analytics.  They also create opportunities to follow-up with users to learn more about their research interests with respect to the digital library resources. Reference inquiries are often collected and recorded on an ad hoc basis, being as they’re often submitted via email, telephone, or in person.

Timing is everything

For institutional use, it can be useful to collect and analyze metrics at times that coincide with both annual, library-wide internal reviews and external reporting events (like for ARL reports, LibQual statistics, and so on). That way, you can reuse the metrics collected for both purposes.

For end user-facing metrics, the delivery of stats should be immediate. For example, “This item has been downloaded x times” is a metric that’s more useful when reported in real time. If manual intervention is required to prepare metrics (such as to parse server logs for relevant information), metrics should be regularly delivered (i.e. weekly or monthly) and transparently reported, so users can understand what they are looking at and can evaluate the usefulness of those metrics accordingly.

Suggested assessment data sources

Following are recommended tools for getting started with data collection. A holistic evaluation framework that librarians might also find useful is JISC’s Toolkit for the Impact of Digitised Scholarly Resources (TIDSR).

Web server logs

• Metrics reported: downloads, referring sites, visits, pageviews, limited demographic information.
• Be sure to export information at regular intervals, as consistent collection is important for longitudinal analysis.
• Web server log data often adhere to certain formats (Apache Custom Log Format, W3C Extended File Log Format, etc.) and can be processed and visualized for human consumption with the help of tools like Webalizer, AWStats, and Tableau.
• Tableau is especially useful for web server log analysis, grouping, and visualization by creating dashboards, user population assessment, and usage over time.

Google Analytics

• Metrics reported: downloads, referring sites, visits, pageviews, limited demographic information.
• Google Analytics has some dashboard functionality that’s useful for saving elements you want to review regularly. GA is also useful for longitudinal analysis, showing big trends in traffic and use.
• For digital collections: Szajewski (2013) has written an excellent guide to using the tool to measure the value of digital special collections.

Citation databases

• Metrics reported: peer-reviewed journals and books that cite digital library resources.
• Citations can be be sourced from subscription databases like Scopus and Web of Science, or from free platforms like Google Scholar and Google Books. Often, specialized searches are required to find citations to digital library content: “cited reference search” can be used in Web of Science, and free-text search for digital library or collections’ names can be employed in other databases to find references to digital library content.
• Citations are much easier to track when persistent identifiers like DOIs are used by whomever is citing digital library content.

Altmetric Explorer for Institutions

• Metrics reported: Shares, saves/favorites, adaptations, mentions, and some other quantitative and qualitative data sourced from the social web. For a full list of Altmetric’s data sources, check out our website.
• Altmetric collects data from across the web related to any scholarly outputs, including any content in a subscriber’s digital special collection (no persistent identifier necessary) which can be displayed in embeddable badges on item records.
• Altmetric provides important qualitative data behind the numbers we report. For example, in addition to seeing that items in your digital library have been mentioned 17 times on Wikipedia, you can also see exactly what has been written about them.

Altmetrics data via social media APIs

• It is also technically possible for digital libraries to connect with individual social media platforms’ APIs to search for mentions of their content. In theory, one could monitor social media sites for mentions of relevant URLs.
• The main drawback to this option is the the developer time required to build customized solutions for each digital library. It could possibly result in much duplicated effort.
• Another possible drawback are the limitations placed on search APIs by platforms themselves; for example, researchers have pointed out that Twitter’s search API is typically restricted to fetching data from only the previous week, and the API’s rate limits restrict the retrieval of large amounts of data at once.

Qualitative data via Google Alerts and Mention

• Track when your content has been shared on the web by setting a Google Alert or Mention alert for your:
  • digital library’s name
  • digital library’s base URL (http://collection1.libraries.psu.edu/cdm/singleitem/collection/amc/id/314/),
  • your repository’s Handle or DOI shoulder (https://scholarworks.iu.edu/dspace/handle/2022/9564 and http://hdl.handle.net/2022/9564; http://dx.doi.org/10.5061/dryad.pp67h and http://datadryad.org/resource/doi:10.5061/dryad.pp67h), or
  • special URLs created for collections (http://webapp1.dlib.indiana.edu/vwwp/welcome.do)
• For important collections, you might also want to set alerts for titles or names relevant to those collections (i.e. for Penn State’s “Advertising Trade Cards from the Alice Marshall Women’s History Collection,” they might also set alerts for “Advertising Trade Cards” and “Alice Marshall”).
• Google Alerts is free to use; Mention is a subscription service.

In Summary

Most digital libraries currently use relatively basic assessment strategies, often ones that are thin on evidence for how collections are being used. Adding altmetrics, citation data, contextual information, and other data to assessment practices could vastly increase libraries’ understanding of how their digital collections are being used and by whom. A number of free and subscription tools can make it easy to automate the collection of data, and analyzing the resulting data at regular intervals can keep assessment projects manageable.

Further reading

• Konkiel, Stacy; Dalmau, Michelle; Scherer, David (2015): “Altmetrics and analytics for digital special collections and institutional repositories.” https://dx.doi.org/10.6084/m9.figshare.1392140.v1
• “Biodiversity Heritage Library highlight online attention surrounding their books with Altmetric badges”, July 2015, Altmetric blog

The post 15 types of data to collect when assessing your digital library appeared first on Altmetric.